Video-Based Neonatal Pain Assessment in Uncontrolled Conditions

Objective: Neonatal pain can have long-term adverse effects on newborns' cognitive and neurological development. Video-based N eonatal P ain A ssessment (NPA) method has gained increasing attention due to its performance and practicality. However, existing methods focus on assessment under cont...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2024-01, Vol.28 (1), p.239-250
Main Authors: Zhu, Huaiyu, Zhao, Yisheng, Chen, Xiaofei, Luo, Feixiang, Mei, Lingli, Chen, Shuohui, Pan, Yun
Format: Article
Language:English
Subjects:
Annotations
body movement
Cognitive ability
Datasets
Disturbances
Faces
facial expression
Feature extraction
Hospitals
Interference
Modules
Neonate pain
Neonates
Occlusion
Pain
Pediatrics
Robustness
uncontrolled conditions
video-based pain assessment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Objective: Neonatal pain can have long-term adverse effects on newborns' cognitive and neurological development. Video-based N eonatal P ain A ssessment (NPA) method has gained increasing attention due to its performance and practicality. However, existing methods focus on assessment under controlled environments while ignoring real-life disturbances present in uncontrolled conditions. Methods: We propose a video-based NPA method, which is robust to four real-life disturbances and adaptively highlights keyframes. Our method involves a region-channel-attention module for extracting facial features under the disturbances of facial occlusion and pose variation; a body language analysis module robust to disturbances from body occlusion and movement interference, which utilizes skeleton sequences to represent the neonate's body; and a keyframes-aware convolution to get rid of information located at non-contributing moments. For evaluation, we built an NPA video dataset of 1091 neonates with disturbance annotations. Results: The results show that our method consistently outperforms state-of-the-art methods on the full dataset and nine subsets, where it achieves an accuracy of 91.04% on the full dataset with an accuracy increment of 6.27%. Contributions: We present the problem of video-based NPA under uncontrolled conditions, propose a method robust to four disturbances, and construct a video NPA dataset, thus facilitating the practical applications of NPA.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2023.3324537